Currently in the art, there are a number of different ways to geolocate a mobile device.
For instance, conventional technologies often use a Global Positioning System (GPS) device to determine the positions (location data) of mobile communication devices thereafter transmitting the locations data, preferably via a wireless network, to targeted businesses. While this approach may give accurate information, battery life of a device may be degraded by GPS technology. Further, GPS devices might be deemed too intrusive by some customers.
In addition, providers of wireless communication services have installed mobile device location capabilities into their networks. In operation, these network overlay location systems take measurements on radio frequency (“RF”) transmissions from mobile devices at base station locations surrounding the mobile device and estimate the location of the mobile device with respect to the base stations using well-known triangulation methods. Because the geographic location of the base stations is known, the determination of the location of the mobile device with respect to the base station permits the geographic location of the mobile device to be determined. However, environments in which a user of the mobile device travels freely, such as a road network, pose a special challenge for accurate location determination. Typically non-stationary users will not have enough measurements associated with their devices at a single base geographic location for precise triangulation.
In general, measurements provided by base stations are often corrupted by white noise. It is known in the art that various smoothing techniques, such as Kalman filtering, may be used to improve estimation results. The Kalman filter produces values that tend to be closer to the true values of the measurements and their associated calculated values by predicting an estimate of uncertainty of the predicted value via a weighted average of the predicted and measured values. When using a Kalman filter to filter noisy location estimates, it is assumed that error terms and measurements have a Gaussian distribution. However, location estimates received from different sources (e.g., different types of sensors) may be multi-modal. As a result, filtering the multi-modal location estimates using a single Kalman filter may yield a location estimate that is inconsistent and erratic over time. Furthermore. Kalman filtering does not take into account the terrain and road networks over which the user travels.